A Guide to Learning Software Trace and Log Analysis Patterns Course

Module 1: Introduction to Tracing & Logging

1 week

• Topics: Roles of traces vs. metrics vs. logs; log formats (JSON, key-value); centralized vs. local storage

• Hands-on: Instrument a sample microservice to emit structured logs

Module 2: Log Collection & Aggregation

1 week

• Topics: Log shippers (Fluentd, Logstash), queues (Kafka), storage backends (Elasticsearch, S3)

• Hands-on: Deploy a Fluentd pipeline shipping logs to Elasticsearch

Module 3: Analysis Patterns & Queries

1 week

• Topics: Search queries, filtering, faceting; common patterns: request tracing, error rate spikes, slow-query identification

• Hands-on: Write Kibana queries to detect service-level errors and correlate them with latency spikes

Module 4: Visualization & Dashboards

1 week

• Topics: Dashboard design principles, time-series charts, anomaly detection visualizations

• Hands-on: Build a real-time dashboard tracking throughput, error rates, and 95ᵗʰ latency percentile

Module 5: Correlation & Distributed Tracing Basics

1 week

• Topics: Trace IDs, span contexts, sampling strategies; integration with OpenTelemetry or Zipkin

• Hands-on: Instrument a multi-service workflow to propagate trace IDs and visualize spans

Module 6: Alerting & Automation

1 week

• Topics: Threshold alerts, anomaly detection rules, integration with PagerDuty/Slack

• Hands-on: Configure alerts on error surges and latency regressions

Module 7: Advanced Topics & Best Practices

1 week

• Topics: Log retention policies, index lifecycle management, cost optimization, security considerations

• Hands-on: Implement ILM policies in Elasticsearch to roll over and purge old logs

Module 8: Capstone Project

1 week

• Topics: End-to-end observability solution design and implementation

• Hands-on: Build a full tracing and logging pipeline for a sample e-commerce app, including dashboards and alert rules

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